Unverified50% confidenceFactExact time
The DFT matrix itself is dense, with its energy uniformly distributed across all frequencies, so low-rank or sparse decomposition necessarily introduces approximation error.
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50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
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Related Claims
UnverifiedThe convergence rate is simultaneously affected by the data dimension d_x and the conditioning dimension d_y, which is consistent with the curse of dimensionality in high-dimensional generative problems.67% similarUnverifiedFlattening a matrix and feeding it to an MLP results in a non-fixed input dimension due to variable row counts, and breaks permutation invariance.65% similarUnverifiedLow-rank decomposition and quantization compression are orthogonally complementary dimensions; quantizing the smaller matrices A and B separately results in smaller errors, and the combined compression ratio can reach several times that of either method alone64% similarUnverifiedOpenAI's method decomposes the discrete Fourier transform (DFT) matrix into a product of several low-rank or sparse matrices, and uses random sampling or structured pruning to skip butterfly operations with extremely small contributions.64% similarVerifiedThe Fast Fourier Transform reduces the computational complexity of the DFT from the naive O(N²) to O(N log N).64% similar
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